code: def _normalize_pan(pan, ms, bfs_indices, n_processed):
      """
      Normalize the pan matrix profile nearest neighbor distances (inplace) relative
      to the corresponding subsequence length from which they were computed
  
      Parameters
      ----------
      pan : numpy.ndarray
          The pan matrix profile
  
      ms : numpy.ndarray
          The breadth-first-search sorted subsequence window sizes
  
      bfs_indices : numpy.ndarray
          The breadth-first-search indices
  
      n_processed : numpy.ndarray
          The number of subsequence window sizes and breadth-first-search indices to
          normalize
  
      Returns
      -------
      None
      """
      idx = bfs_indices[:n_processed]
      norm = 1.0 / (2.0 * np.sqrt(ms[:n_processed]))
      pan[idx] = np.minimum(1.0, pan[idx] * np.expand_dims(norm, 1))
  
file_path: stumpy/stumpy/stimp.py
imports: import numpy
  from .None import core, scrump
  from .aamp_stimp import aamp_stimp, aamp_stimped
  from .stump import stump
  from .stumped import stumped
label_desc: Use image node_id 1 for calling a global function with example usage: _normalize_pan(pan, ms, bfs_indices, n_processed) without return types